FedLin (Mitra et al., 2021) overcomes device heterogeneity with client-specific learning rates and adds an extra gradient correction term to the local loss function to tackle data heterogeneity. As mentioned earlier, here we note that local updates, on the other hand, cannot be treated equally...
Implementation of the full weighted loss for Species Distribution Modeling From the paper: On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learning The implementation of the full weighted loss function L full-weighted , as well as other baseline loss ...
In this testing, weights are applied to the loss function, or a weighted loss is used. This testing is conducted due to class imbalance, where the number of samples in each class is not balanced in the mixed dataset. In this testing, the data is divided into two sets: one with 7 clas...
【论文题目】Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning 今天继续介绍基于值函数的多智能体强化学习 (MARL) 算法——WQMIX[1]。一听这名字就知道这个算法是QMIX[2]的改进版本,如果对QMIX算法不是很熟悉,建议先了解一下本专栏中上一篇关于QMIX算法的解...
learning (DDROWL) that can handle big and complex data. This is a machine learning tool that directly estimates the optimal decision rule and achieves the best of three worlds: deep learning, double robustness, and residual weighted learning. Two architectures have been implemented in the proposed...
and the weighting weights of the same class are the same. the welm has the advantage of short training time and good generalization ability and can efficiently execute classification by optimizing the loss function of weight matrix. as a result, the welm classifier was used to predict dtis by ...
The total loss function of a MTL model with random-weighted loss, therefore, can be calculated as:(4)Ltotal(y^1...y^k,y1...yk)=∑j=1k(−∑iyijlog(y^ij)*pj) For the special case when K=2, the weights for the two learning tasks can be simply decided as:(5)Ltotal(y^1,...
Fast learning rates of OWL associated with least square loss, exponential-hinge loss and r-norm SVM loss are derived explicitly. We also consider the case of unbounded clinical outcome. Fast learning rates are given by imposing some moment conditions ...
A recent approach for unsupervised representation learning attempts to remedy the problem by introducing a mutual information-based loss function (the Spatiotemporal Deep InfoMax) (Anand et al., 2020). In this way, a convolutional neural network will generate feature vectors from RGB-images that ...
Formulates pruning as an optimization problem, where the goal is to search for weights that minimize the loss function while satisfying the pruning cost constraints. This irregular sparsity poses challenges in efficiently utilizing libraries such as the Basic Linear Algebra Subprograms. In contrast, ...